{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d1a73ab",
   "metadata": {},
   "source": [
    "# Tabularize time series\n",
    "\n",
    "In this assignment, your task is to convert **time series data** into a **tabular data set**.\n",
    "\n",
    "You need to create suitable input features from a time series containing weekly sales to be able to forecast sales for the next week.\n",
    "\n",
    "To prepare the dataset for this assignment, please follow the guidelines in the notebook `02-create-online-retail-II-datasets.ipynb` in the `01-Create-Datasets` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0c0f390",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from feature_engine.creation import CyclicalFeatures\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f53976d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-06</th>\n",
       "      <td>213000.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-13</th>\n",
       "      <td>195810.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-20</th>\n",
       "      <td>182396.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-27</th>\n",
       "      <td>22007.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sales\n",
       "week                 \n",
       "2009-12-06  213000.35\n",
       "2009-12-13  195810.04\n",
       "2009-12-20  182396.74\n",
       "2009-12-27   22007.77\n",
       "2010-01-03       0.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load weekly sales dataset\n",
    "\n",
    "filename = \"../../Datasets/online_retail_dataset.csv\"\n",
    "\n",
    "df = pd.read_csv(\n",
    "    filename,\n",
    "    usecols=[\"week\", \"United Kingdom\"],\n",
    "    parse_dates=[\"week\"],\n",
    "    index_col=[\"week\"],\n",
    ")\n",
    "\n",
    "df.columns = ['sales']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdfe9415",
   "metadata": {},
   "source": [
    "## Plot time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "daaea155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='week'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=[15, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ceabd79",
   "metadata": {},
   "source": [
    "## Missing data\n",
    "\n",
    "Check if there are missing values in the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdcd1435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c484bca",
   "metadata": {},
   "source": [
    "## Missing timestamps\n",
    "\n",
    "Check if there are missing timestamps in the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72ddc8a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Introduce the missing timestamps.\n",
    "# Note that we capture this in a different dfframe.\n",
    "df_ = df.asfreq(\"1W\")\n",
    "\n",
    "# Apply the forward fill method.\n",
    "df_imputed = df_.fillna(method=\"ffill\")\n",
    "\n",
    "# plot the time series.\n",
    "ax = df_.plot(linestyle=\"-\", marker=\".\", figsize=[15, 4])\n",
    "\n",
    "# plot the imputed values on top, in red.\n",
    "df_imputed[df_.isnull()].plot(ax=ax, legend=None, marker=\".\", color=\"r\")\n",
    "\n",
    "# Add title.\n",
    "plt.title(\"Sales\")\n",
    "\n",
    "# the y axis label\n",
    "plt.ylabel(\"Sales\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444ca303",
   "metadata": {},
   "source": [
    "## Seasonality\n",
    "\n",
    "Does the time series show any obvious seasonal pattern?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88095645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average sales per month.\n",
    "\n",
    "month = df.index.month\n",
    "\n",
    "df.groupby(month)[\"sales\"].mean().plot(figsize=(10, 5))\n",
    "plt.title(\"Mean sales per month\")\n",
    "plt.ylabel(\"Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8c1ca36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average sales for every 1,2,3,4 and 5 week of a month.\n",
    "\n",
    "wom = (df.index.day - 1) // 7 + 1\n",
    "\n",
    "df.groupby(wom)[\"sales\"].mean().plot(figsize=(10, 5))\n",
    "plt.title(\"Mean sales per week of month\")\n",
    "plt.ylabel(\"Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81565cb",
   "metadata": {},
   "source": [
    "# Feature engineering\n",
    "\n",
    "Now, let's begin to tabularize the data.\n",
    "\n",
    "## Datetime features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f570136f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-06</th>\n",
       "      <td>213000.35</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-13</th>\n",
       "      <td>195810.04</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-20</th>\n",
       "      <td>182396.74</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-27</th>\n",
       "      <td>22007.77</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sales  month\n",
       "week                        \n",
       "2009-12-06  213000.35     12\n",
       "2009-12-13  195810.04     12\n",
       "2009-12-20  182396.74     12\n",
       "2009-12-27   22007.77     12\n",
       "2010-01-03       0.00      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# month of the year\n",
    "\n",
    "df[\"month\"] = df.index.month\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f191b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# week of month\n",
    "\n",
    "df['wom'] = (df.index.day -1) //7 + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3366b63",
   "metadata": {},
   "source": [
    "## Cyclical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6854504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>month</th>\n",
       "      <th>wom</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>wom_sin</th>\n",
       "      <th>wom_cos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-06</th>\n",
       "      <td>213000.35</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.951057</td>\n",
       "      <td>0.309017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-13</th>\n",
       "      <td>195810.04</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.587785</td>\n",
       "      <td>-0.809017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-20</th>\n",
       "      <td>182396.74</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.587785</td>\n",
       "      <td>-0.809017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-27</th>\n",
       "      <td>22007.77</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.951057</td>\n",
       "      <td>0.309017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.951057</td>\n",
       "      <td>0.309017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sales  month  wom     month_sin  month_cos   wom_sin   wom_cos\n",
       "week                                                                          \n",
       "2009-12-06  213000.35     12    1 -2.449294e-16   1.000000  0.951057  0.309017\n",
       "2009-12-13  195810.04     12    2 -2.449294e-16   1.000000  0.587785 -0.809017\n",
       "2009-12-20  182396.74     12    3 -2.449294e-16   1.000000 -0.587785 -0.809017\n",
       "2009-12-27   22007.77     12    4 -2.449294e-16   1.000000 -0.951057  0.309017\n",
       "2010-01-03       0.00      1    1  5.000000e-01   0.866025  0.951057  0.309017"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyclical = CyclicalFeatures(\n",
    "    variables=[\"month\", \"wom\"],  # The features to transform.\n",
    "    drop_original=False,  # Whether to drop the original features.\n",
    ")\n",
    "\n",
    "df = cyclical.fit_transform(df)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78dc5cf2",
   "metadata": {},
   "source": [
    "## Lag features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9beb4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>month</th>\n",
       "      <th>wom</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>wom_sin</th>\n",
       "      <th>wom_cos</th>\n",
       "      <th>sales_lag_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-06</th>\n",
       "      <td>213000.35</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.951057</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-13</th>\n",
       "      <td>195810.04</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.587785</td>\n",
       "      <td>-0.809017</td>\n",
       "      <td>213000.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-20</th>\n",
       "      <td>182396.74</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.587785</td>\n",
       "      <td>-0.809017</td>\n",
       "      <td>195810.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-27</th>\n",
       "      <td>22007.77</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.951057</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>182396.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.951057</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>22007.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sales  month  wom     month_sin  month_cos   wom_sin  \\\n",
       "week                                                                   \n",
       "2009-12-06  213000.35     12    1 -2.449294e-16   1.000000  0.951057   \n",
       "2009-12-13  195810.04     12    2 -2.449294e-16   1.000000  0.587785   \n",
       "2009-12-20  182396.74     12    3 -2.449294e-16   1.000000 -0.587785   \n",
       "2009-12-27   22007.77     12    4 -2.449294e-16   1.000000 -0.951057   \n",
       "2010-01-03       0.00      1    1  5.000000e-01   0.866025  0.951057   \n",
       "\n",
       "             wom_cos  sales_lag_1  \n",
       "week                               \n",
       "2009-12-06  0.309017          NaN  \n",
       "2009-12-13 -0.809017    213000.35  \n",
       "2009-12-20 -0.809017    195810.04  \n",
       "2009-12-27  0.309017    182396.74  \n",
       "2010-01-03  0.309017     22007.77  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sales the previous week.\n",
    "\n",
    "tmp = pd.DataFrame(df[\"sales\"].shift(freq=\"1W\"))\n",
    "\n",
    "# Name for the new variable.\n",
    "tmp.columns = [\"sales_lag_1\"]\n",
    "\n",
    "# Add the variable to the original df.\n",
    "df = df.merge(tmp, left_index=True, right_index=True, how=\"left\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7110899",
   "metadata": {},
   "source": [
    "## Window features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53fd60ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>month</th>\n",
       "      <th>wom</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>wom_sin</th>\n",
       "      <th>wom_cos</th>\n",
       "      <th>sales_lag_1</th>\n",
       "      <th>sales_2_mean</th>\n",
       "      <th>sales_2_max</th>\n",
       "      <th>sales_2_min</th>\n",
       "      <th>sales_4_mean</th>\n",
       "      <th>sales_4_max</th>\n",
       "      <th>sales_4_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-06</th>\n",
       "      <td>213000.350</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.510565e-01</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-13</th>\n",
       "      <td>195810.040</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.877853e-01</td>\n",
       "      <td>-0.809017</td>\n",
       "      <td>213000.350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-20</th>\n",
       "      <td>182396.740</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-5.877853e-01</td>\n",
       "      <td>-0.809017</td>\n",
       "      <td>195810.040</td>\n",
       "      <td>204405.1950</td>\n",
       "      <td>213000.350</td>\n",
       "      <td>195810.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-27</th>\n",
       "      <td>22007.770</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.510565e-01</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>182396.740</td>\n",
       "      <td>189103.3900</td>\n",
       "      <td>195810.040</td>\n",
       "      <td>182396.740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>9.510565e-01</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>22007.770</td>\n",
       "      <td>102202.2550</td>\n",
       "      <td>182396.740</td>\n",
       "      <td>22007.770</td>\n",
       "      <td>153303.72500</td>\n",
       "      <td>213000.35</td>\n",
       "      <td>22007.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>112318.850</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>5.877853e-01</td>\n",
       "      <td>-0.809017</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11003.8850</td>\n",
       "      <td>22007.770</td>\n",
       "      <td>0.000</td>\n",
       "      <td>100053.63750</td>\n",
       "      <td>195810.04</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-17</th>\n",
       "      <td>111460.470</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-5.877853e-01</td>\n",
       "      <td>-0.809017</td>\n",
       "      <td>112318.850</td>\n",
       "      <td>56159.4250</td>\n",
       "      <td>112318.850</td>\n",
       "      <td>0.000</td>\n",
       "      <td>79180.84000</td>\n",
       "      <td>182396.74</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-24</th>\n",
       "      <td>82065.331</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-9.510565e-01</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>111460.470</td>\n",
       "      <td>111889.6600</td>\n",
       "      <td>112318.850</td>\n",
       "      <td>111460.470</td>\n",
       "      <td>61446.77250</td>\n",
       "      <td>112318.85</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>110790.591</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>82065.331</td>\n",
       "      <td>96762.9005</td>\n",
       "      <td>111460.470</td>\n",
       "      <td>82065.331</td>\n",
       "      <td>76461.16275</td>\n",
       "      <td>112318.85</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-07</th>\n",
       "      <td>95340.552</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.510565e-01</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>110790.591</td>\n",
       "      <td>96427.9610</td>\n",
       "      <td>110790.591</td>\n",
       "      <td>82065.331</td>\n",
       "      <td>104158.81050</td>\n",
       "      <td>112318.85</td>\n",
       "      <td>82065.331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sales  month  wom     month_sin  month_cos       wom_sin  \\\n",
       "week                                                                        \n",
       "2009-12-06  213000.350     12    1 -2.449294e-16   1.000000  9.510565e-01   \n",
       "2009-12-13  195810.040     12    2 -2.449294e-16   1.000000  5.877853e-01   \n",
       "2009-12-20  182396.740     12    3 -2.449294e-16   1.000000 -5.877853e-01   \n",
       "2009-12-27   22007.770     12    4 -2.449294e-16   1.000000 -9.510565e-01   \n",
       "2010-01-03       0.000      1    1  5.000000e-01   0.866025  9.510565e-01   \n",
       "2010-01-10  112318.850      1    2  5.000000e-01   0.866025  5.877853e-01   \n",
       "2010-01-17  111460.470      1    3  5.000000e-01   0.866025 -5.877853e-01   \n",
       "2010-01-24   82065.331      1    4  5.000000e-01   0.866025 -9.510565e-01   \n",
       "2010-01-31  110790.591      1    5  5.000000e-01   0.866025 -2.449294e-16   \n",
       "2010-02-07   95340.552      2    1  8.660254e-01   0.500000  9.510565e-01   \n",
       "\n",
       "             wom_cos  sales_lag_1  sales_2_mean  sales_2_max  sales_2_min  \\\n",
       "week                                                                        \n",
       "2009-12-06  0.309017          NaN           NaN          NaN          NaN   \n",
       "2009-12-13 -0.809017   213000.350           NaN          NaN          NaN   \n",
       "2009-12-20 -0.809017   195810.040   204405.1950   213000.350   195810.040   \n",
       "2009-12-27  0.309017   182396.740   189103.3900   195810.040   182396.740   \n",
       "2010-01-03  0.309017    22007.770   102202.2550   182396.740    22007.770   \n",
       "2010-01-10 -0.809017        0.000    11003.8850    22007.770        0.000   \n",
       "2010-01-17 -0.809017   112318.850    56159.4250   112318.850        0.000   \n",
       "2010-01-24  0.309017   111460.470   111889.6600   112318.850   111460.470   \n",
       "2010-01-31  1.000000    82065.331    96762.9005   111460.470    82065.331   \n",
       "2010-02-07  0.309017   110790.591    96427.9610   110790.591    82065.331   \n",
       "\n",
       "            sales_4_mean  sales_4_max  sales_4_min  \n",
       "week                                                \n",
       "2009-12-06           NaN          NaN          NaN  \n",
       "2009-12-13           NaN          NaN          NaN  \n",
       "2009-12-20           NaN          NaN          NaN  \n",
       "2009-12-27           NaN          NaN          NaN  \n",
       "2010-01-03  153303.72500    213000.35    22007.770  \n",
       "2010-01-10  100053.63750    195810.04        0.000  \n",
       "2010-01-17   79180.84000    182396.74        0.000  \n",
       "2010-01-24   61446.77250    112318.85        0.000  \n",
       "2010-01-31   76461.16275    112318.85        0.000  \n",
       "2010-02-07  104158.81050    112318.85    82065.331  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here I take the average, maximum and minimum values\n",
    "# of the previous 2 and 4 weeks.\n",
    "\n",
    "for window in [2, 4]:\n",
    "    \n",
    "    tmp = df[\"sales\"].rolling(window=window).agg([\"mean\", \"max\", \"min\"]).shift(1)\n",
    "\n",
    "    tmp.columns = [f\"sales_{window}_{func}\" for func in [\"mean\", \"max\", \"min\"]]\n",
    "    \n",
    "    df = df.merge(tmp, left_index=True, right_index=True, how=\"left\")\n",
    "\n",
    "# view of the result\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59d5a89",
   "metadata": {},
   "source": [
    "## Drop missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21bf6649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales           0.000000\n",
       "month           0.000000\n",
       "wom             0.000000\n",
       "month_sin       0.000000\n",
       "month_cos       0.000000\n",
       "wom_sin         0.000000\n",
       "wom_cos         0.000000\n",
       "sales_lag_1     0.009434\n",
       "sales_2_mean    0.018868\n",
       "sales_2_max     0.018868\n",
       "sales_2_min     0.018868\n",
       "sales_4_mean    0.037736\n",
       "sales_4_max     0.037736\n",
       "sales_4_min     0.037736\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find fraction of missing data.\n",
    "\n",
    "df.isnull().sum() / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3da3e350",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ae8079",
   "metadata": {},
   "source": [
    "## Split data\n",
    "\n",
    "Separate the data into training and testing sets, leaving the data after the last week of September to evaluate the forecasts, that is, in the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "659771af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2010-01-03 00:00:00'), Timestamp('2011-12-11 00:00:00'))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.min(), df.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f9b76e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((91, 14), (11, 14))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data in time.\n",
    "\n",
    "X_train = df[df.index <= \"2011-09-30\"]\n",
    "X_test = df[df.index > \"2011-09-30\"]\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "28389d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the target variable\n",
    "y_train = X_train[\"sales\"].copy()\n",
    "y_test = X_test[\"sales\"].copy()\n",
    "\n",
    "# the input features\n",
    "X_train = X_train.drop(\"sales\", axis=1)\n",
    "X_test = X_test.drop(\"sales\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820803d5",
   "metadata": {},
   "source": [
    "## Naive forecast\n",
    "\n",
    "Predict sales in the next week (t) as the value of sales in the previous week (t-1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0b2d36e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train rmse:  45844.30745262792\n",
      "test rmse:  65327.34284108387\n"
     ]
    }
   ],
   "source": [
    "# Performance of naive forecast\n",
    "\n",
    "print(\"train rmse: \", mean_squared_error(\n",
    "    y_train, X_train[\"sales_lag_1\"], squared=False,))\n",
    "\n",
    "print(\"test rmse: \",  mean_squared_error(\n",
    "    y_test, X_test[\"sales_lag_1\"], squared=False,))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4058260e",
   "metadata": {},
   "source": [
    "## Machine Learning\n",
    "\n",
    "### Random forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1de016f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train rmse:  38630.62589645069\n",
      "test rmse:  57764.311865163814\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(\n",
    "    n_estimators=5,\n",
    "    max_depth=1,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Performance of random forests\n",
    "\n",
    "print(\"train rmse: \", mean_squared_error(\n",
    "    y_train, rf_model.predict(X_train), squared=False,))\n",
    "\n",
    "print(\"test rmse: \",  mean_squared_error(\n",
    "    y_test, rf_model.predict(X_test), squared=False,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7556b13a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Importance')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's now plot the feature's importance\n",
    "# according to the random forests.\n",
    "\n",
    "# Create series with feature importance.\n",
    "tmp = pd.Series(rf_model.feature_importances_)\n",
    "\n",
    "# Let's add the variable names.\n",
    "tmp.index = X_train.columns\n",
    "\n",
    "# Let's make a bar plot.\n",
    "tmp.plot.bar(figsize=(10, 5), rot=45)\n",
    "plt.title(\"Feature importance\")\n",
    "plt.ylabel(\"Importance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42154a90",
   "metadata": {},
   "source": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb62bab1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train rmse:  34836.61713978724\n",
      "test rmse:  54602.94605229004\n"
     ]
    }
   ],
   "source": [
    "linear_model = Lasso(alpha=100, random_state=0)\n",
    "\n",
    "linear_model.fit(X_train, y_train)\n",
    "\n",
    "# Performance of linear model\n",
    "\n",
    "print(\"train rmse: \", mean_squared_error(\n",
    "    y_train, linear_model.predict(X_train), squared=False,))\n",
    "\n",
    "print(\"test rmse: \",  mean_squared_error(\n",
    "    y_test, linear_model.predict(X_test), squared=False,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "56f44220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Importance')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's now plot the feature's importance\n",
    "# according to the linear model.\n",
    "from numpy import abs\n",
    "\n",
    "# Create series with feature importance.\n",
    "tmp = pd.Series(abs(linear_model.coef_))\n",
    "\n",
    "# Let's add the variable names.\n",
    "tmp.index = X_train.columns\n",
    "\n",
    "# Let's make a bar plot.\n",
    "tmp.plot.bar(figsize=(10, 5), rot=45)\n",
    "plt.title(\"Feature importance\")\n",
    "plt.ylabel(\"Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4957673a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
